PosteriorEDTRProbs: Convert Treatment Sequence Draws into Embedded Dynamic Treatment Regime Draws

Description

Apply Robins' G-computation formula to compute the embedded dynamic treatment regime draws from as a weighted average of treatment sequence and stage-1 response probability draws.

If design is "design-1", then compute for Design 1 SMART with 6 embedded treatment sequences and 4 embedded dynamic treatment regimes.

If design is "general", then compute for General SMART with 8 embedded treatment sequences and 8 embedded dynamic treatment regimes.

If design is "design-3", then compute for Design 3 SMART with 9 embedded treatment sequences and 6 embedded dynamic treatment regimes.

Usage

PosteriorEDTRProbs(x, design = "design-1")

Arguments

A data frame consisting of draws from the posterior of the end of study response probabilities of each treatment sequence and of stage-1 response probabilities for each stage-1 treatment

design

Which SMART design to compute the posterior draws for: "design-1" or "general" or "design-3".

Value

Matrix of EDTR specific posterior response probability draws at the end of the study There will be 4 columns for design-1, 8 columns for design general, and 6 columns for design-3 each corresponding to an EDTR. The number of rows will be the same as that of x.

Details

For the General SMART design, x should have columns p_1, p_2, p_3, p_4, p_5, p_6, p_7, p_8, s1, and s2.

For the Design-1 SMART, x should have columns p_1, p_2, p_3, p_4, p_5, p_6, s1, and s2.

For the design-3 SMART, x should have columns

p_1, p_2, p_3, p_4, p_5, p_6, p_7, p_8, p_9 s1, s2, and s3

These are the posterior draws of the response probabilities for each treatment sequence and stage-1 response probability draws.

s1 contains the draws of the stage-1 response probability for the first treatment, s2 is analogous for the second treatment and s3 for the third treatment.

Examples

Run this code

# NOT RUN {
dat <- SimDesign1(sample_size=250,
                  response_prob = c(0.5,0.9,0.3,0.7,0.5,0.8),
                  stage_one_trt_one_response_prob = 0.7,
                  stage_one_trt_two_response_prob = 0.4)

x <- PosteriorTrtSeqProb(niter = 1000, dat, design = "design-1")

PosteriorEDTRProbs(x, design = "design-1")
# }